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生成式人工智能赋能乡村产业人才振兴:逻辑、挑战与路径

Generative artificial intelligence empowering rural industrial talent revitalization: logic, challenges, and implementation pathways

  • 摘要: 在数智时代背景下,审视生成式人工智能(GenAI)赋能乡村产业人才振兴议题,是推进高质量发展与中国式现代化的内在要求。本文系统阐述了GenAI赋能乡村产业人才振兴的内在逻辑,分析了其面临的现实挑战,并提出了相应的实现策略。研究发现,GenAI通过“工具、模式、生态”三个维度重塑乡村产业人才发展体系,其实践逻辑呈现出“从场景赋能到实践能力内生构建”的路径,并最终指向“从人才赋能到乡村振兴价值回归”的战略目标。然而,技术与数据适配性不足、制度支持机制不完善、成本与投入机制短板以及嵌入社会的结构性张力等多重因素制约了GenAI的有效落地。基于此,本文提出应从构建本土化产业数据库与场景适配模型、建立系统化政策支持与标准化应用框架、搭建分层培育体系与智能决策支持平台、创新成本共担与可持续运营机制、健全以人为本的技术治理与知识保护机制等关键环节,探索GenAI赋能乡村产业人才振兴的系统路径,为政策制定与实践实施提供参考。

     

    Abstract: In the context of the digital intelligence era, examining the empowerment of rural industrial talent revitalization by generative artificial intelligence (GenAI) constitutes an inherent requirement for advancing high-quality development and Chinese-style modernization. This paper systematically expounds the internal logic through which GenAI empowers rural industrial talent revitalization, analyzes the practical challenges it encounters, and proposes corresponding implementation strategies. The study finds that GenAI reshapes the rural industrial talent development system across three dimensions: tools, models, and ecosystems. Its practical logic demonstrates a progressive pathway from scenario-based empowerment to the endogenous construction of practical capabilities, ultimately orienting toward the strategic objective of realizing the value return of rural revitalization through talent empowerment. However, the effective implementation of GenAI is constrained by multiple factors, including insufficient alignment between technologies and data, incomplete institutional support mechanisms, weaknesses in cost and investment structures, and structural tensions embedded within social systems. In response, this paper proposes a systematic pathway for empowering rural industrial talent revitalization through GenAI by focusing on several key aspects: constructing localized industrial databases and scenario-adaptive models; establishing systematic policy support and standardized application frameworks; developing tiered cultivation systems and intelligent decision-support platforms; innovating cost-sharing and sustainable operational mechanisms; and improving human-centered technology governance and knowledge protection mechanisms. These efforts aim to provide theoretical support and practical references for policy formulation and implementation.

     

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